Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

ACM Computing Surveys 2026 Open access

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Authors: Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
DOI: 10.1145/3833867
Cited by: 3
References: 379

Abstract

We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and construct taxonomies of deepfake generation and detection methods, illustrating the important groups of methods. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing detectors on the most popular datasets. In addition, we develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content. The results indicate that state-of-the-art detectors fail to generalize to deepfakes generated by unseen generators. Our project page and new benchmark are available at https://github.com/CroitoruAlin/biodeep.

Fields of Study & Subjects

Generative Adversarial Networks and Image Synthesis Digital Media Forensic Detection

Publication details

Journal / source
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Volume
58
Issue
15
Pages
1–35
Publication date
23 July 2026
Language
EN